---
title: "Coronavirus en Chubut (Argentina)"
author: "Ministerio de Salud de Chubut"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    # social: ["facebook", "twitter", "linkedin"]
    source_code: embed
    vertical_layout: fill
---

```{r setup, include=FALSE}
#------------------ Packages ------------------
library(flexdashboard)
# install.packages("devtools")
# devtools::install_github("RamiKrispin/coronavirus", force = TRUE)
library(coronavirus)
data(coronavirus)
update_datasets()
# View(coronavirus)
# max(coronavirus$date)

`%>%` <- magrittr::`%>%`
#------------------ Parameters ------------------
# Set colors
# https://www.w3.org/TR/css-color-3/#svg-color
confirmed_color <- "purple"
active_color <- "#1f77b4"
recovered_color <- "forestgreen"
death_color <- "red"
#------------------ Data ------------------
# df <- coronavirus %>%
#   # dplyr::filter(date == max(date)) %>%
#   dplyr::filter(Country.Region == "Argentina") %>%
#   dplyr::group_by(Country.Region, type) %>%
#   dplyr::summarise(total = sum(cases)) %>%
#   tidyr::pivot_wider(
#     names_from = type,
#     values_from = total
#   ) %>%
#   # dplyr::mutate(unrecovered = confirmed - ifelse(is.na(recovered), 0, recovered) - ifelse(is.na(death), 0, death)) %>%
#   dplyr::mutate(unrecovered = confirmed - ifelse(is.na(death), 0, death)) %>%
#   dplyr::arrange(-confirmed) %>%
#   dplyr::ungroup() %>%
#   dplyr::mutate(country = dplyr::if_else(Country.Region == "United Arab Emirates", "UAE", Country.Region)) %>%
#   dplyr::mutate(country = dplyr::if_else(country == "Mainland China", "China", country)) %>%
#   dplyr::mutate(country = dplyr::if_else(country == "North Macedonia", "N.Macedonia", country)) %>%
#   dplyr::mutate(country = trimws(country)) %>%
#   dplyr::mutate(country = factor(country, levels = country))
df <- read.csv2("/mnt/28EC75CE33EA541A/temporales/Coronavirus/estadisticas/tablas_de_entrada/df.csv")

# df_daily <- coronavirus %>%
#   dplyr::filter(Country.Region == "Argentina") %>%
#   dplyr::group_by(date, type) %>%
#   dplyr::summarise(total = sum(cases, na.rm = TRUE)) %>%
#   tidyr::pivot_wider(
#     names_from = type,
#     values_from = total
#   ) %>%
#   dplyr::arrange(date) %>%
#   dplyr::ungroup() %>%
#   #dplyr::mutate(active = confirmed - death - recovered) %>%
#   dplyr::mutate(active = confirmed - death) %>%
#   dplyr::mutate(
#     confirmed_cum = cumsum(confirmed),
#     death_cum = cumsum(death),
#     # recovered_cum = cumsum(recovered),
#     active_cum = cumsum(active)
#   )
df_daily <- read.csv2("/mnt/28EC75CE33EA541A/temporales/Coronavirus/estadisticas/tablas_de_entrada/df_daily.csv")

# df1 <- coronavirus %>% dplyr::filter(date == max(date))
df1 <- read.csv2("/mnt/28EC75CE33EA541A/temporales/Coronavirus/estadisticas/tablas_de_entrada/df1.csv")

```

Summary
=======================================================================

Row {data-width=400}
-----------------------------------------------------------------------

### confirmed {.value-box}

```{r}

valueBox(
  value = paste(format(sum(df$confirmed), big.mark = ","), "", sep = " "),
  caption = "Total confirmed cases",
  icon = "fas fa-user-md",
  color = confirmed_color
)
```
















### death {.value-box}

```{r}

valueBox(
  value = paste(format(sum(df$death, na.rm = TRUE), big.mark = ","), " (",
    round(100 * sum(df$death, na.rm = TRUE) / sum(df$confirmed), 1),
    "%)",
    sep = ""
  ),
  caption = "Death cases (death rate)",
  icon = "fas fa-heart-broken",
  color = death_color
)
```


Row
-----------------------------------------------------------------------

### **Daily cumulative cases by type** (Argentina only)
    
```{r}
plotly::plot_ly(data = df_daily) %>%
  plotly::add_trace(
    x = ~date,
    # y = ~active_cum,
    y = ~confirmed_cum,
    type = "scatter",
    mode = "lines+markers",
    # name = "Active",
    name = "Confirmed",
    line = list(color = active_color),
    marker = list(color = active_color)
  ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~death_cum,
    type = "scatter",
    mode = "lines+markers",
    name = "Death",
    line = list(color = death_color),
    marker = list(color = death_color)
  ) %>%
  plotly::add_annotations(
    x = as.Date("2020-02-04"),
    y = 1,
    text = paste("First case"),
    xref = "x",
    yref = "y",
    arrowhead = 5,
    arrowhead = 3,
    arrowsize = 1,
    showarrow = TRUE,
    ax = -10,
    ay = -90
  ) %>%
  plotly::add_annotations(
    x = as.Date("2020-03-11"),
    y = 3,
    text = paste("First death"),
    xref = "x",
    yref = "y",
    arrowhead = 5,
    arrowhead = 3,
    arrowsize = 1,
    showarrow = TRUE,
    ax = -90,
    ay = -90
  ) %>%
  plotly::add_annotations(
    x = as.Date("2020-03-18"),
    y = 14,
    text = paste(
      "New containment",
      "",
      "measures"
    ),
    xref = "x",
    yref = "y",
    arrowhead = 5,
    arrowhead = 3,
    arrowsize = 1,
    showarrow = TRUE,
    ax = -10,
    ay = -90
  ) %>%
  plotly::layout(
    title = "",
    yaxis = list(title = "Cumulative number of cases"),
    xaxis = list(title = "Date"),
    legend = list(x = 0.1, y = 0.9),
    hovermode = "compare"
  )
```

Comparison
=======================================================================


Column {data-width=400}
-------------------------------------


### **Daily new confirmed cases**
    
```{r}
# daily_confirmed <- coronavirus %>%
#   dplyr::filter(type == "confirmed") %>%
#   dplyr::filter(date >= "2020-02-29") %>%
#   dplyr::mutate(country = Country.Region) %>%
#   dplyr::group_by(date, country) %>%
#   dplyr::summarise(total = sum(cases)) %>%
#   dplyr::ungroup() %>%
#   tidyr::pivot_wider(names_from = country, values_from = total)
# 
# #----------------------------------------
# # Plotting the data
# 
# daily_confirmed %>%
#   plotly::plot_ly() %>%
#   plotly::add_trace(
#     x = ~date,
#     y = ~Argentina,
#     type = "scatter",
#     mode = "lines+markers",
#     name = "Argentina"
#   ) %>%
#   plotly::add_trace(
#     x = ~date,
#     y = ~France,
#     type = "scatter",
#     mode = "lines+markers",
#     name = "France"
#   ) %>%
#   plotly::add_trace(
#     x = ~date,
#     y = ~Spain,
#     type = "scatter",
#     mode = "lines+markers",
#     name = "Spain"
#   ) %>%
#   plotly::add_trace(
#     x = ~date,
#     y = ~Italy,
#     type = "scatter",
#     mode = "lines+markers",
#     name = "Italy"
#   ) %>%
#   plotly::layout(
#     title = "",
#     legend = list(x = 0.1, y = 0.9),
#     yaxis = list(title = "Number of new confirmed cases"),
#     xaxis = list(title = "Date"),
#     # paper_bgcolor = "black",
#     # plot_bgcolor = "black",
#     # font = list(color = 'white'),
#     hovermode = "compare",
#     margin = list(
#       # l = 60,
#       # r = 40,
#       b = 10,
#       t = 10,
#       pad = 2
#     )
#   )


comparativo_provincias <- read.csv2("/mnt/28EC75CE33EA541A/temporales/Coronavirus/estadisticas/tablas_de_entrada/comparativo_provincias_acumulados.csv", sep= ";")
#colnames(comparativo_provincias)

library(viridis)
comparativo_provincias %>% ###### daily_confirmed
  plotly::plot_ly() %>%
  plotly::add_trace(
    x = ~date,
    y = ~Ciudad.Autónoma.de.Buenos.Aires,
    type = "scatter",  #, type = 'scatter', mode = 'none', stackgroup = 'one', fillcolor = '#F5FF8D')
    mode = "none", ####lines+markers
    stackgroup = 'one',
    fillcolor = viridis(24)[1],
    name = "Ciudad Autónoma de Buenos Aires"
  ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~Buenos.Aires,
    type = "scatter",  #, type = 'scatter', mode = 'none', stackgroup = 'one', fillcolor = '#F5FF8D')
    mode = "none", ####lines+markers
    stackgroup = 'one',
    fillcolor = viridis(24)[2],
    name = "Buenos.Aires"
  ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~Catamarca,
    type = "scatter",  #, type = 'scatter', mode = 'none', stackgroup = 'one', fillcolor = '#F5FF8D')
    mode = "none", ####lines+markers
    stackgroup = 'one',
    fillcolor = viridis(24)[3],
    name = "Catamarca"
  ) %>%
  
  plotly::add_trace(
    x = ~date,
    y = ~Chaco,
    type = "scatter",  #, type = 'scatter', mode = 'none', stackgroup = 'one', fillcolor = '#F5FF8D')
    mode = "none", ####lines+markers
    stackgroup = 'one',
    fillcolor = viridis(24)[4],
    name = "Chaco"
  ) %>%
  
  plotly::add_trace(
    x = ~date,
    y = ~Chubut,
    type = "scatter",  #, type = 'scatter', mode = 'none', stackgroup = 'one', fillcolor = '#F5FF8D')
    mode = "none", ####lines+markers
    stackgroup = 'one',
    fillcolor = viridis(24)[5],
    name = "Chubut"
  ) %>%
  
  plotly::add_trace(
    x = ~date,
    y = ~Córdoba,
    type = "scatter",  #, type = 'scatter', mode = 'none', stackgroup = 'one', fillcolor = '#F5FF8D')
    mode = "none", ####lines+markers
    stackgroup = 'one',
    fillcolor = viridis(24)[6],
    name = "Córdoba"
  ) %>%
  
  plotly::add_trace(
    x = ~date,
    y = ~Corrientes,
    type = "scatter",  #, type = 'scatter', mode = 'none', stackgroup = 'one', fillcolor = '#F5FF8D')
    mode = "none", ####lines+markers
    stackgroup = 'one',
    fillcolor = viridis(24)[7],
    name = "Corrientes"
  ) %>%
  
  plotly::add_trace(
    x = ~date,
    y = ~Entre.Ríos,
    type = "scatter",  #, type = 'scatter', mode = 'none', stackgroup = 'one', fillcolor = '#F5FF8D')
    mode = "none", ####lines+markers
    stackgroup = 'one',
    fillcolor = viridis(24)[8],
    name = "Entre Ríos"
  ) %>%
  
  plotly::add_trace(
    x = ~date,
    y = ~Formosa,
    type = "scatter",  #, type = 'scatter', mode = 'none', stackgroup = 'one', fillcolor = '#F5FF8D')
    mode = "none", ####lines+markers
    stackgroup = 'one',
    fillcolor = viridis(24)[9],
    name = "Formosa"
  ) %>%
  
  plotly::add_trace(
    x = ~date,
    y = ~Jujuy,
    type = "scatter",  #, type = 'scatter', mode = 'none', stackgroup = 'one', fillcolor = '#F5FF8D')
    mode = "none", ####lines+markers
    stackgroup = 'one',
    fillcolor = viridis(24)[10],
    name = "Jujuy"
  ) %>%
  
  plotly::add_trace(
    x = ~date,
    y = ~La.Pampa,
    type = "scatter",  #, type = 'scatter', mode = 'none', stackgroup = 'one', fillcolor = '#F5FF8D')
    mode = "none", ####lines+markers
    stackgroup = 'one',
    fillcolor = viridis(24)[11],
    name = "La Pampa"
  ) %>%
  
  plotly::add_trace(
    x = ~date,
    y = ~La.Rioja,
    type = "scatter",  #, type = 'scatter', mode = 'none', stackgroup = 'one', fillcolor = '#F5FF8D')
    mode = "none", ####lines+markers
    stackgroup = 'one',
    fillcolor = viridis(24)[12],
    name = "La Rioja"
  ) %>%
  
  plotly::add_trace(
    x = ~date,
    y = ~Mendoza,
    type = "scatter",  #, type = 'scatter', mode = 'none', stackgroup = 'one', fillcolor = '#F5FF8D')
    mode = "none", ####lines+markers
    stackgroup = 'one',
    fillcolor = viridis(24)[13],
    name = "Mendoza"
  ) %>%
  
  plotly::add_trace(
    x = ~date,
    y = ~Misiones,
    type = "scatter",  #, type = 'scatter', mode = 'none', stackgroup = 'one', fillcolor = '#F5FF8D')
    mode = "none", ####lines+markers
    stackgroup = 'one',
    fillcolor = viridis(24)[14],
    name = "Misiones"
  ) %>%
  
  plotly::add_trace(
    x = ~date,
    y = ~Neuquén,
    type = "scatter",  #, type = 'scatter', mode = 'none', stackgroup = 'one', fillcolor = '#F5FF8D')
    mode = "none", ####lines+markers
    stackgroup = 'one',
    fillcolor = viridis(24)[15],
    name = "Neuquén"
  ) %>%
  
  plotly::add_trace(
    x = ~date,
    y = ~Río.Negro,
    type = "scatter",  #, type = 'scatter', mode = 'none', stackgroup = 'one', fillcolor = '#F5FF8D')
    mode = "none", ####lines+markers
    stackgroup = 'one',
    fillcolor = viridis(24)[16],
    name = "Río Negro"
  ) %>%
  
  plotly::add_trace(
    x = ~date,
    y = ~Salta,
    type = "scatter",  #, type = 'scatter', mode = 'none', stackgroup = 'one', fillcolor = '#F5FF8D')
    mode = "none", ####lines+markers
    stackgroup = 'one',
    fillcolor = viridis(24)[17],
    name = "Salta"
  ) %>%
  
  plotly::add_trace(
    x = ~date,
    y = ~San.Juan,
    type = "scatter",  #, type = 'scatter', mode = 'none', stackgroup = 'one', fillcolor = '#F5FF8D')
    mode = "none", ####lines+markers
    stackgroup = 'one',
    fillcolor = viridis(24)[18],
    name = "San Juan"
  ) %>%
  
  plotly::add_trace(
    x = ~date,
    y = ~San.Luis,
    type = "scatter",  #, type = 'scatter', mode = 'none', stackgroup = 'one', fillcolor = '#F5FF8D')
    mode = "none", ####lines+markers
    stackgroup = 'one',
    fillcolor = viridis(24)[19],
    name = "San Luis"
  ) %>%
  
  plotly::add_trace(
    x = ~date,
    y = ~Santa.Cruz,
    type = "scatter",  #, type = 'scatter', mode = 'none', stackgroup = 'one', fillcolor = '#F5FF8D')
    mode = "none", ####lines+markers
    stackgroup = 'one',
    fillcolor = viridis(24)[20],
    name = "Santa Cruz"
  ) %>%
  
  plotly::add_trace(
    x = ~date,
    y = ~Santa.Fe,
    type = "scatter",  #, type = 'scatter', mode = 'none', stackgroup = 'one', fillcolor = '#F5FF8D')
    mode = "none", ####lines+markers
    stackgroup = 'one',
    fillcolor = viridis(24)[21],
    name = "Santa Fe"
  ) %>%
  
  plotly::add_trace(
    x = ~date,
    y = ~Santiago.del.Estero,
    type = "scatter",  #, type = 'scatter', mode = 'none', stackgroup = 'one', fillcolor = '#F5FF8D')
    mode = "none", ####lines+markers
    stackgroup = 'one',
    fillcolor = viridis(24)[22],
    name = "Santiago del Estero"
  ) %>%
  
  plotly::add_trace(
    x = ~date,
    y = ~Tierra.del.Fuego,
    type = "scatter",  #, type = 'scatter', mode = 'none', stackgroup = 'one', fillcolor = '#F5FF8D')
    mode = "none", ####lines+markers
    stackgroup = 'one',
    fillcolor = viridis(24)[23],
    name = "Tierra del Fuego"
  ) %>%
  
  plotly::add_trace(
    x = ~date,
    y = ~Tucumán,
    type = "scatter",  #, type = 'scatter', mode = 'none', stackgroup = 'one', fillcolor = '#F5FF8D')
    mode = "none", ####lines+markers
    stackgroup = 'one',
    fillcolor = viridis(24)[24],
    name = "Tucumán"
  ) %>%
  
  plotly::layout(
    title = "",
    legend = list(x = 0.1, y = 0.9),
    yaxis = list(title = "Number of new confirmed cases"),
    xaxis = list(title = "Date"),
    # paper_bgcolor = "black",
    # plot_bgcolor = "black",
    # font = list(color = 'white'),
    hovermode = "compare",
    margin = list(
      # l = 60,
      # r = 40,
      b = 10,
      t = 10,
      pad = 2
    )
  )


```
 



























































Map
=======================================================================

### **World map of cases** (*use + and - icons to zoom in/out*)

```{r}
# map tab added by Art Steinmetz
library(leaflet)
library(leafpop)
library(purrr)

df1 <- read.csv2("/mnt/28EC75CE33EA541A/temporales/Coronavirus/estadisticas/tablas_de_entrada/df1.csv", sep=",")
df1$Long <- as.numeric(paste(df1$Long))
df1$Lat <- as.numeric(paste(df1$Lat))

cv_data_for_plot <- df1 %>%
  # dplyr::filter(Country.Region == "Argentina") %>%
  dplyr::filter(cases > 0) %>%
  dplyr::group_by(Country.Region, Province.State, Lat, Long, type) %>%
  dplyr::summarise(cases = sum(cases)) %>%
  dplyr::mutate(log_cases = 2 * log(cases)) %>%
  dplyr::ungroup()
cv_data_for_plot.split <- cv_data_for_plot %>% split(cv_data_for_plot$type)
pal <- colorFactor(c("orange", "red", "green"), domain = c("confirmed", "death", "recovered"))
map_object <- leaflet() %>% addProviderTiles(providers$Stamen.Toner)
names(cv_data_for_plot.split) %>%
  purrr::walk(function(df) {
    map_object <<- map_object %>%
      addCircleMarkers(
        data = cv_data_for_plot.split[[df]],
        lng = ~Long, lat = ~Lat,
        #                 label=~as.character(cases),
        color = ~ pal(type),
        stroke = FALSE,
        fillOpacity = 0.8,
        radius = ~log_cases,
        popup = leafpop::popupTable(cv_data_for_plot.split[[df]],
          feature.id = FALSE,
          row.numbers = FALSE,
          zcol = c("type", "cases", "Country.Region", "Province.State")
        ),
        group = df,
        #                 clusterOptions = markerClusterOptions(removeOutsideVisibleBounds = F),
        labelOptions = labelOptions(
          noHide = F,
          direction = "auto"
        )
      )
  })

map_object %>%
  addLayersControl(
    overlayGroups = names(cv_data_for_plot.split),
    options = layersControlOptions(collapsed = FALSE)
  )
```





About
=======================================================================

**The Coronavirus Dashboard: the case of Argentina**

This Coronavirus dashboard: the case of Argentina provides an overview of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) epidemic for Argentina. This dashboard is built with R using the R Makrdown framework and was adapted from this [dashboard](https://ramikrispin.github.io/coronavirus_dashboard/){target="_blank"} by Rami Krispin.

**Code**

The code behind this dashboard is available on [GitHub](https://github.com/AntoineSoetewey/coronavirus_dashboard){target="_blank"}.

**Data**

The input data for this dashboard is the dataset available from the [`{coronavirus}`](https://github.com/RamiKrispin/coronavirus){target="_blank"} R package. Make sure to download the development version of the package to have the latest data:

```
install.packages("devtools")
devtools::install_github("RamiKrispin/coronavirus")
```

The data and dashboard are refreshed on a daily basis.

The raw data is pulled from the Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus [repository](https://github.com/RamiKrispin/coronavirus-csv){target="_blank"}.

**Contact**

For any question or feedback, you can [contact me](https://www.statsandr.com/contact/). More information about this dashboard can be found in this [article](https://www.statsandr.com/blog/how-to-create-a-simple-coronavirus-dashboard-specific-to-your-country-in-r/).

**Update**

The data is as of `r format(max(coronavirus$date), "%A %B %d, %Y")` and the dashboard has been updated on `r format(Sys.time(), "%A %B %d, %Y")`.



*Go back to [www.statsandr.com](https://www.statsandr.com/) (blog) or [www.antoinesoetewey.com](https://www.antoinesoetewey.com/) (personal website)*.